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Loan Use Draft V35 NBER WORKING PAPER SERIES FOLLOW THE MONEY: METHODS FOR IDENTIFYING CONSUMPTION AND INVESTMENT RESPONSES TO A LIQUIDITY SHOCK Dean Karlan Adam Osman Jonathan Zinman Working Paper 19696 http://www.nber.org/papers/w19696 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 December 2013 The authors thank financial support from the Bill and Melinda Gates Foundation, the Consultative Group for Assistance to the Poor (CGAP) and AusAID. The authors thank Kareem Haggag, Romina Kazadjian, Megan McGuire, Faith McCollister, Mark Miller, and Sarah Oberst at Innovations for Poverty Action for project management and field support throughout the project, and the senior management and staff at First Macro Bank and FICO Bank for their support and collaboration throughout this project. The authors retained full intellectual freedom to report and interpret the results throughout the study. All errors and opinions are those of the authors. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2013 by Dean Karlan, Adam Osman, and Jonathan Zinman. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Follow the Money: Methods for Identifying Consumption and Investment Responses to a Liquidity Shock Dean Karlan, Adam Osman, and Jonathan Zinman NBER Working Paper No. 19696 December 2013 JEL No. D12,D92,G21,O12,O16 ABSTRACT Identifying the impacts of liquidity shocks on spending decisions is difficult methodologically but important for theory, practice, and policy. Using seven different methods on microenterprise loan applicants, we find striking results. Borrowers report uses of loan proceeds strategically, and more generally their reporting depends on elicitation method. Borrowers also interpret loan use questions differently than the key counterfactual: spending that would not have occurred sans loan. We identify the counterfactual using random assignment of loan approvals and short-run follow-up elicitation of major household and business cash outflows, and estimate that about 100% of loan-financed spending is on business inventory. Dean Karlan Jonathan Zinman Department of Economics Department of Economics Yale University Dartmouth College P.O. Box 208269 314 Rockefeller Hall New Haven, CT 06520-8629 Hanover, NH 03755 and NBER and NBER [email protected] [email protected] Adam Osman Yale University 27 Hillhouse Avenue New Haven, CT 06511 [email protected] I. Introduction What are the impacts of liquidity shocks on the consumption and investment decisions of households and small businesses? Answers to this question have implications for the theory, practice, and regulation of credit, as well as for modeling intertemporal consumer choice. They shed light on perceived returns to investment, and on the extent to which constraints bind more for some types of household spending than others. Estimating impacts of liquidity shocks matters in many domains, for example in understanding household leveraging and deleveraging decisions in the wake of credit supply shocks,1 as well as evaluating interventions such as business grants,2 unconditional cash transfers,3 and microcredit expansions.4 Papers that track responses to liquidity shocks often focus on estimating medium- and long- term effects by measuring spending patterns, balance sheets, or summary statistics of financial conditions several months or years post-shock. This reduced-form evidence has proven quite useful, but it often leaves the mechanism underlying any change unidentified. For each possible state of the world many months post-liquidity shock -- high enterprise growth relative to baseline, low enterprise growth, consumption growth, etc. -- there are many paths from the liquidity change to that outcome. Identifying mechanisms is important because different paths can have different welfare implications. To take an example closest to the setting we examine in this paper, many microcredit impact evaluations do not find significant effects of microcredit on enterprise scale or profitability one or two years post-intervention, even when the loans are targeted to those who are microentrepreneurs at baseline.5 There are at least three possible explanations for these findings: 1) impacts only materialize over longer horizons due to compounded benefits, adjustment, etc. This hypothesis often motivates researchers and program advocates to highlight the value of longer-term outcome data; 2) microentrepreneurs do not actually invest marginal liquidity in their businesses, perhaps because they are credit constrained on the margin and have household investment or consumption smoothing with a higher expected return on investment (in utility terms) than business investment; 3) microentrepreneurs do invest microloan proceeds in their businesses, but these investments do not end up earning a positive net return. The second and third explanations highlight the value of very short-run data on spending decisions post-shock: “following the money” from liquidity to spending decisions can reveal the 1 See e.g. Hall (2011), Eggertsson and Krugman (2012), and Mian and Sufi (2012). 2 See e.g. Fafchamps et al (2013), Karlan, Knight and Udry (2013), and de Mel, McKenzie and Woodruff (2008). 3 See e.g. Benhassine et al (2013), Blattman, Fiala and Martinez (2012), Haushofer and Shapiro (2013), Karlan et al. (2013). 4 See e.g. Angelucci, Karlan and Zinman (2013), Attanasio et al (2011) , Augsburg et al (2012), Banerjee et al (2013), Crepon et al (2011), Karlan and Zinman (2010), Karlan and Zinman (2011), and Tarozzi, Desai and Johnson (2013). 5 See the studies cited in the previous footnote, with the exception of Karlan and Zinman (2010), which examines untargeted consumer loans. 2 mechanisms underlying the paths from shock to outcomes. If the second explanation is accurate that motivates further attempts to identify causes, consequences, and cures for credit constraints. If the third explanations is accurate that motivates further attempts to understand why entrepreneurs make investments that, ex-post at least, do not yield a positive net return on average (Moskowitz and Vissing-Jorgensen 2002; Anagol, Etang, and Karlan 2013; Karlan, Knight, and Udry 2013).6 To take another example, Mian and Sufi (2011) find that borrowing against rising home values by existing homeowners drove a significant fraction of both the rise in U.S. household leverage from 2002 to 2006 and the increase in mortgage defaults from 2006 to 2008. How did homeowners deploy the borrowed funds? As the paper explains (p.2134): The real effects of the home equity–based borrowing channel depend on what households do with the borrowed money. We find no evidence that borrowing in response to increased house prices is used to purchase new homes or investment properties. In fact, home equity–based borrowing is not used to pay down expensive credit card balances, even for households with a heavy dependence on credit card borrowing. Given the high cost of keeping credit card balances, this result suggests a high marginal private return to borrowed funds. Knowing what sort of spending generates this high marginal private return would inform how economists specify consumer preferences, expectations, and other inputs into consumer choice models. For example, spend data would help distinguish liquidity constraints from self-control problems as drivers of leveraging, which Mian and Sufi highlight as a fruitful avenue for future research (p.2155).7 As both examples suggest, unpacking the mechanisms underlying the long-run effects of a liquidity shock may require data on consumption and investment choices immediately after the shock. If one can follow the money from liquidity shock to spending, it may help identify how households use liquidity to try to improve their lots. But how exactly one might go about measuring spending in the immediate aftermath of a liquidity shock is not immediately obvious, methodologically speaking. There are several challenges. Administrative data is rarely available for the right sample, timeframe, or spending frequency, and even more rarely sufficiently comprehensive in its coverage of different types of consumption and investment. This makes survey design very important. Yet money is fungible, and household and (micro)enterprise balance sheets are often complex, so it may be cognitively difficult for survey respondents to identify the effects of the liquidity shock on their spending, relative to the counterfactual of no shock. Similarly, surveys that simply ask about past purchases 6 Now consider the opposite state of the world: say an evaluation of 12-month impacts does find that a microcredit expansion produces larger, more profitable businesses. The mechanism need not be investment in business assets per se (inventory, physical capital, etc.) Rather, it could be investments in human capital (training, health, child care, etc.) that enable the entrepreneur or business “helpers” from her family to be more productive. 7 For related inquiries see Bauer et al (2012), and Bhutta and Keys (2013). 3 produce noisy data, and measurement error increases with the length of the
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